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Community Based Participatory Research
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Selection of Dr. William T. Riley as the Director of the Office of Behavioral and Social Sciences Research, NIH
July 30, 2015

OBSSR 20th Anniversary Celebration
April 09, 2015

2015 Training Institute for Dissemination and Implementation Research in Health
April 10, 2015

2015 UCLA Summer Institute on Mobile Health (mHealth) Technology Research
February 23, 2015

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February 10, 2015

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November 5, 2015
BSSR Lecture Series - Video Games and Neuroscience: A Vision of the Future of Medicine and Education
2:00pm - 3:00pm
Bethesda, MD

December 4, 2015
BSSR Lecture Series - Good Behavior: Sharing, and Reusing Research Video
2:00pm - 3:00pm
Bethesda, MD

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Home > Scientific AreasMethodologymHealth > mHealth Webinar Series

mHealth Webinar Series



Speeding Up Translational Science with Mobile Tools and Emerging Technologies

nih mhealth logoPraduman Jain, BS, MS
CEO, Vignet Inc.

October 7, 2014

PDF image Presentation Slides (PDF 1.65MB)

FitNinja, is a Mobile Health Research Toolkit developed through contracts from the National Cancer Institute (NCI).  FitNinja is a clinical and research platform for creating, extending and disseminating evidence-based programs beyond the research or clinic walls. Researchers and providers can create customized programs from a toolkit of emerging technologies to study many different health contexts with just one single system. The HIPAA-compliant, secure system is cloud-hosted and built for cost-efficient dissemination and scalability. The programs are easily disseminated through mobile devices, iPads and laptops for patients, care teams, coaches, caregivers and family members.

FitNinja was designed to address the need for the next generation of health and healthcare technology research through advances in patient generated data collected via patient reported outcomes, ecological momentary assessments, wireless sensors (passively and actively sensed data), PROMIS surveys and validated measures. These form novel longitudinal patient records that are accessed in real time through novel data visualizations, data exports and downloadable spreadsheets. The data visualizations drive knowledge and insights which in turn lead to further refinements of interventions and program configuration.

This webinar will explore the utility of systems like FitNinja for supporting the mHealth community. These systems reduce the need for building one-off stand-alone apps or new software, by providing a tool that allows researchers focus on running creative studies.  FitNinja was developed to handle a wide range of research topics, including: behavior change, population surveillance, care giver research, remote monitoring, community behavioral participatory research, critical care or other interests.

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From a Sugar Rush to a Connected Health High

nih mhealth logoAnand Iyer, Ph.D., MBA
Chief Data Science Officer, WellDoc, Inc.

September 17, 2014

PDF image Presentation Slides (PDF 4.39MB)

Years of research in diabetes self-management has suggested that many adults with the disease are not optimally managing their blood glucose control or the other parameters that affect their disease such as medications and lifestyle. The reasons for these self-management difficulties are numerous and include, but are not limited to: misinformation or spotty health literacy, poor adherence to medications, a lack of timely and contextually-relevant feedback, and the absence of a personalized coaching system. Without supports such as these, it is a challenge for people to manage their complex regimen. This webinar will describe the development of the BlueStar diabetes system. For anecdotes, BlueStar is the first and only mobile prescription therapy:  that is, a mobile software solution that has 1) published significant outcomes in cluster randomized control studies, 2) been cleared by the FDA, and 3) is prescribed by a healthcare provider, dispensed by a pharmacy and reimbursed by a health insurance plan.

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Smart and Connected Health: A Joint NSF/NIH Initiative

Rebecca SchnallWendy Nilsen, Ph.D.
Program Director, Smart and Connected Health, NSF and
Office of Behavioral and Social Sciences Research, NIH

September 9, 2014

PowerPoint Slides with Audio (PPTX 54.64MB)

PDF image Presentation Slides (PDF 2.14MB)

The Institutes and Centers of the National Institutes of Health (NIH) and the National Science Foundation (NSF) have identified Smart and Connected Health (SCH; NSF-13-543) as a research priority. SCH was designed to address the need for the next generation of health and healthcare technology research through advances in the understanding of and applications in information science, technology, behavior, cognition, sensors, robotics, bioimaging and engineering. The purpose of this webinar is to explore this interagency program announcement and identify examples of the type of projects that have been funded through the program.

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What is in the Toolbox? Mobile Health Solutions for Rehabilitation Medicine

Rebecca SchnallRon Poropatich, MD
Executive Director of the Center for Military Medicine Research, Health Sciences
University of Pittsburgh

June 30, 2014

PDF image Presentation Slides (PDF 4.39MB)

PDF image Presentation Video

This presentation will cover a range of mHealth applications that supports the home health phase of rehabilitation for traumatic brain injury (TBI), spinal cord injury, amputee care and psychological stress. Topics include sleep assessment, cognitive training, vestibular and ocular training for TBI, and prosthetic mHealth tools for amputees. A comprehensive approach that focuses on individualized, self-management applications will be stressed.



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Leveraging mobile technology to improve clinical outcomes and the quality of research data within the epilepsy community

Rebecca SchnallRobert Moss, Co-Founder

May 13, 2014 at 2pm

Presentation Slide (PDF 3.39MB)

With an initial focus of improving patient/doctor communication about seizure activity and surrounding treatments, has expanded into improving the quality and cost of  collecting data in epilepsy related research as well. Launched in 2007, the Seizure Tracker system serves a global audience with a registered user base of over 18,000. Largely due to the expansion into a mobile platform, Seizure Tracker users record between 700 - 1000 seizure events daily. Mobile technology allows for real-time capture of event related chronic medical conditions and enables easy sharing of information into larger care systems. Seizure Tracker has seamlessly integrated the use of a mobile application to record the length of a seizure while videotaping the event as it happens. The events can then be synced with a larger web-based system. The website allows users to enter surrounding treatments including medications and diet therapies. also allows for recording possible influences on seizure activity like hormonal fluctuations, missed medications, etc. While on the site, users can create reports which include graphical comparisons of the information entered into the areas of Seizure Tracker. These reports can then be easily emailed or printed and shared with the care team. The creation of Seizure Tracker has changed the way patients log seizures by providing a platform to record more accurate seizure data that is easily shared with their care providers. This presentation will highlight the use of technology to fill gaps in doctor/patient communication, how mobile technology can impact the quality of patient reported outcomes and how that data can impact clinical care along with research.

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Adolescents' Use and Perceived Usefulness of Mobile Technology for Meeting their Health Information Needs and Improving Adherence to Improved Health Behaviors

Rebecca SchnallRebecca Schnall, RN PhD
Assistant Professor of Nursing,
Columbia University
New York, NY
February 19, 2014

PowerPoint Slides with Audio (PPTX 84.4MB)
Presentation Slide (PDF 84.4MB)

Research on health information has primarily focused on the needs of adults or parents of children with chronic illnesses or consumers. There is limited research on the health information needs of adolescents and the use of technology for meeting those needs. This is particularly important as the use of mobile technology has made a huge impact on communication, access, and information/resource delivery to adolescents, who are the largest age group of users of this technology. We conducted a series of studies with urban minority adolescents to understand their health information needs, their use of mobile devices for accessing health information and their use of mobile technology for adherence to new health behaviors. The purpose of this presentation will be to discuss adolescents use and perceived usefulness of mobile technology for accessing health information resources and for adhering to improved health behaviors.


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Mobile Health (mHealth): From smart phone apps and sensor streams to behavioral biomarkers

Dr. Kaplan, OBSSR DirectorWe are experiencing some technical difficulty with the webinar footage. We hope to get it resolved soon. In the meantime please find the slides below.

Deborah Estrin, Ph.D.,
Professor of Computer Science,
Cornell NYC Tech
January 29, 2013

PDF image Presentation Slides (PDF 2.43MB)

The most significant health and wellness challenges increasingly involve multiple chronic conditions, from diabetes, hypertension, and asthma to depression, chronic-pain, sleep and neurological disorders. The promise of mobile health (mHealth) is that we can leverage the power and ubiquity of mobile and cloud technologies to monitor and understand symptoms, side effects and treatment outside the clinical setting, thereby closing the feedback loops of self-care, clinical-care, and personal-evidence-creation.

However, to realize this promise, we must develop new data capture, processing and modeling techniques to convert the ‘digital exhaust’ emitted by mobile phone use into behavioral biomarkers. This calls for the sort of modular layered processing framework used in speech and vision in which low level state classifications of raw data (e.g., estimated activity states such as sitting, walking, driving from continuous accelerometer and location traces), are used to derive mid-level semantic features (e.g., total number of ambulatory minutes, number of hours spent out of house), that can then be mapped to particular behavioral biomarkers for specific diseases (e.g., chronic pain, GI dysfunction, MS, fatigue, depression, etc). The techniques needed to derive these markers will range from simple functions to machine learning classifiers, and will need to fuse diverse data types, but all will need to cope with noisy, erratic data sources.

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